The present invention relates to the field of interactive voice response (IVR) systems and, more particularly, to dynamically adjusting the performance of an IVR system using a complex events processor (CEP).
Interactive Voice Response (IVR) systems are an efficient tool used to direct incoming phone sessions to the desired person or information. The usability of IVR systems is typically tested for general functionality with a homogenized caller base. However, actual callers interacting with the IVR system span a variety of language comprehension capabilities and/or linguistic differences. For example, American callers with a Southern accent can have problems understanding an IVR system using synthesized speech with a Mid-Western United States accent.
Conventional IVR systems lack the intelligence to recognize when a caller is experiencing an interaction difficulty or a problematic event occurs. That is, the IVR system cannot recognize that a specific pattern of caller-entered commands can indicate a problematic event experienced by a caller due to the lack of errors generated during the execution of the software. However, when a specific pattern of caller-entered commands is found to be repeated by various, unrelated callers, it can indicate a performance issue of IVR system.
For example, a main menu may include two commands, B and D, that sound similar but provide unrelated call navigation. Callers often select the incorrect command first due to the audible similarity of the commands. Each menu selection made by the caller is valid, and, therefore, does not trigger an operational error within the IVR system. However, by examining the frequency of the patterns “B, back to main, D” and “D, back to main, B”, it can be concluded that usability of the main menu can be increased, and caller frustration decreased, by selecting an alternate wording for one of the menu commands.
Current approaches to identify the occurrence of problematic events involve post-processing of all the data collected by the IVR system and additional correlation analysis. Due to their labor-intensive nature, these approaches are typically performed in batches, which decreases the timeliness in which the performance issues are identified and resolved.